Bioinspired Optimization of Germination Nutrients Based on Lactuca sativa Seedling Root Traits as Influenced by Seed Stratification, Fortification and Light Spectrums

Abstract

Ecophysiological stimulators directly affect root morphology, especially in the embryonic stage. To enhance crop germination, an understanding of the root traits under abiotic inducers is needed. In this study, the combined impacts of white and red-blue light spectrums, cold stratification, and seed fortification involving various concentrations of bioactive chemicals namely simple nutrient addition program solution, gibberellic acid, α-naphthaleneacetic acid with thiamine hydrochloride were evaluated on loose-leaf lettuce (Lactuca sativa var. Altima) seedling root architecture. The growth-promoting effects of these nutrients varied the growth rate and morphology of roots which are immediately shown during the radicle development. Integrated computer vision and computational intelligence were employed for phytomorphological signatures extraction of seedlings that were cultivated in a customized modulable spectrum experimental chamber (MSPEC). Root phenotype model was developed using graph-cut segmentation and region properties, and the ideal germination nutrient concentration was optimized using bioinspired models with firefly algorithm optimal result of 204.1 mg/L for nitrate, 238.15 mg/L for phosphate, and 158.08 mg/L for potassium. It was verified that lettuce seedlings can endure highly concentrated nutrients, however, it is more sensitive to phosphate as this macronutrient significantly promotes root growth with the increased whorl number on white light spectrum exposure with cold stratification.

Hybrid Genetic Programming and Multiverse-based Optimization of Pre-Harvest Growth Factors of Aquaponic Lettuce Based on Chlorophyll Concentration

“…Lettuce seeds (Ramgo, Philippines) were placed at the chiller unpacked to undergo cold stratification for at least two weeks to break its dormancy. Nutrient film technique was used for the grow bed in both unoptimized and optimized cultivation with maintained nitrate of 124.70 mg L -1 , phosphate of 65.23 mg L -1 , and potassium of 168.74 mg L -1 for six weeks from which the pH and electrical conductivity are controlled between 6 ± 0.5 and 1 ± 0.2 mS cm -1 [6]. For cultivating with an unoptimized environment, diffused gas by equilibrating the inside chamber from the outside chamber.…”

“…This phase employed a non-destructive vision-based chlorophyll-a concentration prediction technique. The nondestructive approach was fully adapted from our previous work using multigene genetic regression programming (MSRGP) model configured with 100 initial population size and generation size, a tournament size of 50 with Pareto rate of 0.2, an elite fraction of 0.1, maximum genes of 10, and tree depth of 5 [6]. This developed model is based on the spectrophotometry experiment (Shimadzu UV-Vis 1900) on light absorption in the 663 nm and 645 nm optical densities of fresh leaf pigment mixed with 75% acetone.…”

“…If the acquired fitness value of the particle is higher than the pbest, then the pbest is updated, and if the acquired fitness value of the pbest is greater than the gbest, then gbest is updated as the best solution at the current iteration and disseminated to all the swarm of particles. It is followed by updating the velocity and position of each particle in the swarm based on (5)(6) where inertial weight (w, 0.4) reduces velocity impact to particle movements and increase exploitation, cognitive coefficient (ϕp, 2) accelerates the influence of pbest in the solution, and social coefficient (ϕg, 1.8) accelerates the influence of gbest in the solution. Hence, the particle velocity and position at time t+1 is affected by the current motion (first term), particle memory influence (second term), and swarm influence (third term) (5)(6).…”

“…Agricultural yield is dominantly driven by the combination of photosynthetic growth factors, namely carbon dioxide concentration, light intensity, temperature level [1], [2], photoperiod [3], vapor pressure [4], and the nutrients coming from soil and water resources that include nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), sulfur (S) as the macronutrients [5]. NPK, iron, and salinity concentration were previously optimized based on germination rate and root morphology for lettuce and rice [6]- [8]. Crops, such as corn, rice, tomato, and lettuce, are vulnerable to climate change resulting in lower biomass production [5], [9], [10].…”

Non-destructive in Situ Measurement of Aquaponic Lettuce Leaf Photosynthetic Pigments and Nutrient Concentration Using Hybrid Genetic Programming

“…Each hole has a 1.5-inch diameter that holds the plastic cup with 1 square inch rockwool. The water nitrate, phosphate, and potassium concentrations were maintained at 124.70 mg/l, 65.23 mg/l, and 168.74 mg/l from germination to harvesting stage (Concepcion II & Dadios, 2021) by adding fractions of simple nutrient addition program (SNAP) solution (Institute of Plant Breeding, University of the Philippines Los Baños) and freshwater through tempering approach. Two artificial light treatments were established, namely red-blue (RB) and white (W) lights, to induce differences in the growth and quality of lettuce (Table 1).…”